Implementing AIOps with Prometheus, Grafana, and ML Training Course

Introduction to AIOps with Open-Source Tools

• Overview of AIOps concepts and their strategic benefits
• The role of Prometheus and Grafana within the observability stack
• Positioning ML in AIOps: contrasting predictive and reactive analytics

Configuring Prometheus and Grafana

• Installation and configuration of Prometheus for time series data collection
• Developing Grafana dashboards utilizing real-time metrics
• Investigating exporters, relabeling, and service discovery mechanisms

Data Preprocessing for Machine Learning

• Extracting and transforming Prometheus metrics for analysis
• Structuring datasets for anomaly detection and forecasting models
• Leveraging Grafana transformations or Python-based pipelines

Applying Machine Learning for Anomaly Detection

• Implementing foundational ML models for outlier detection (e.g., Isolation Forest, One-Class SVM)
• Training and assessing models using time series data
• Displaying detected anomalies within Grafana dashboards

Forecasting Metrics with Machine Learning

• Developing basic forecasting models (introduction to ARIMA, Prophet, and LSTM)
• Anticipating system load and resource consumption
• Utilizing predictions for early warning alerts and scaling decisions

Integrating ML with Alerting and Automation

• Establishing alert rules based on ML outputs or predefined thresholds
• Utilizing Alertmanager and configuring notification routing
• Initiating scripts or automation workflows upon anomaly detection

Scaling and Operationalizing AIOps

• Connecting with external observability tools (e.g., ELK stack, Moogsoft, Dynatrace)
• Operationalizing ML models within observability pipelines
• Best practices for implementing AIOps at scale

Summary and Future Steps